
ABUSE.MOM — BEHAVE OR GET EXPOSED
| Signature | Description | Points | Severity |
|---|---|---|---|
| UA bot: curl | Known bot/crawler User-Agent detected | +40 | |
| UA changed for same IP | Multiple User-Agents — bot rotation technique | +25 | |
| Danger medium hits: 27 | Medium-risk: admin panels, config files | +60 | |
| 404 ratio >= 60% | Majority of requests returned 404 — enumeration | +25 | |
| Probe pattern 302->404 same path | Behavioral anomaly detected by automated analysis | +20 | |
| Burst: 132 req / 2s | Abnormally fast request rate — automated scanning | +35 | |
| Burst: 200 req / 10s | Abnormally fast request rate — automated scanning | +35 | |
| Foreign referer seen | Referer from unrelated external domain | +10 | |
| POST requests present | Behavioral anomaly detected by automated analysis | +8 |
Reconstructed HTTP requests from server access logs. Target domains redacted for security.
* Typical request patterns for detected signatures. Actual target domains are redacted.
Address UA spoofing from 23.91.97.174: maintain blocklist of known malicious UA strings, require consistent UA across sessions, implement TLS fingerprinting.
IP 23.91.97.174 is enumerating directories. Configure fail2ban apache-404 jail after 10+ 404 errors. Disable directory listings. Normalize all 404 responses.
IP 23.91.97.174 is generating excessive traffic. Limit connections per source IP. Enable geographic blocking if traffic from this region is unexpected.
Network reconnaissance data from Shodan. Open ports may indicate running services, misconfigurations, or potential attack surfaces.
| Port | Service | Risk | Description |
|---|---|---|---|
| 80 | HTTP | Low | HTTP web server — standard web traffic |
| CVE ID | Link |
|---|---|
| CVE-2019-9511 | NVD → |
| CVE-2025-23419 | NVD → |
| CVE-2019-9513 | NVD → |
| CVE-2019-9516 | NVD → |
| CVE-2018-16843 | NVD → |
| CVE-2023-44487 | NVD → |
| CVE-2018-16845 | NVD → |
| CVE-2017-20005 | NVD → |
| CVE-2018-16844 | NVD → |
| CVE-2017-7529 | NVD → |
| CVE-2019-20372 | NVD → |
| CVE-2021-3618 | NVD → |
| CVE-2021-23017 | NVD → |
🔴 Security scanning identified 13 vulnerability entries on this host. This volume strongly suggests severely outdated software. Consult NVD advisories for details.
Data source: Shodan InternetDB. Scanned independently of abuse.mom.
This IP was checked against major DNS-based blacklists used by mail servers and firewalls worldwide.
Checked: Spamhaus, SpamCop, Barracuda, SORBS, CBL, UCEProtect. Results may change over time.
23.91.97.174 has been assigned a threat score of 258/100 (Critical). With this rating, the IP falls into the critical severity bracket — among the most dangerous addresses in our monitoring database.
The following attack categories were identified:
Our monitoring infrastructure has identified 23.91.97.174, geolocated to Hong Kong, Hong Kong, operating on the network of Zenlayer Inc, as a source of suspicious network activity. During its 1-day observation window, we recorded 2 hostile requests from this IP — roughly 2 per day on average. This address belongs to a datacenter or cloud hosting provider. Hosting IPs are frequently leveraged by threat actors who rent cheap VPS instances specifically for conducting attacks. The combination of 3 distinct attack vectors indicates a sophisticated, multi-pronged threat actor deploying automated tools that probe multiple attack surfaces simultaneously. Hong Kong currently accounts for 62 blocked IPs in our database, making it a notable source of malicious traffic. A score of 258/100 places this address in the top tier of severity. Block and investigate any historical connections.
This IP belongs to a hosting or data center provider. Malicious traffic from hosting infrastructure often originates from compromised VPS instances, rented servers used for scanning campaigns, or abused free-tier cloud accounts. Hosting providers typically respond to abuse reports within 24-72 hours.
Examining HTTP headers beyond User-Agent reveals attack tools and automated scripts. Missing standard headers, unusual ordering, non-standard values, and inconsistencies with claimed client identity all serve as reliable detection signals.
WAFs inspect HTTP traffic to block common attacks but require careful tuning. Overly aggressive rules cause false positives while permissive configurations miss attacks. Modern WAFs combine signature matching with behavioral analysis and machine learning.